Multi-source Seq2seq guided by knowledge for Chinese healthcare consultation.

Online healthcare consultation offers people a convenient way to consult doctors. In this paper, we aim at building a generative dialog system for Chinese healthcare consultation. As the original Seq2seq architecture tends to suffer the issue of generating low-quality responses, the multi-source Seq2seq architecture generating more informative responses is much more preferred in this task. The multi-source Seq2seq architecture takes advantage of retrieval techniques to obtain responses from the database, and then takes these responses alongside the user-issued question as input. However, some of the retrieved responses might be not much related to the user-issued question, resulting in the generation of unsatisfying responses that are not correct in diagnosis or instead provide inappropriate advice on prevention or treatment. Therefore, this paper proposes multi-source Seq2seq guided by knowledge (MSSGK) to handle this problem. MSSGK differs from the multi-source Seq2seq architecture in that domain knowledge, including disease labels and topic labels about prevention and treatment, is introduced into the response generation via a multi-task learning framework. To better exploit the domain knowledge, we propose three attention mechanisms to provide more appropriate guidance for response generation. Experimental results on a dataset of real-world healthcare consultation show the effectiveness of the proposed method.

Cross domains adversarial learning for Chinese named entity recognition for online medical consultation.

Deep learning methods have been applied to Chinese named entity recognition for the online medical consultation. They require a large number of marked samples. However, no such database is available at present. This paper begins with constructing a larger labelled Chinese texts database for the online medical consultation. Second, a basic framework unit is proposed, which is pre-trained by the transfer learning from both Bidirectional language model and Mask language model trained on the larger unlabelled data. Finally, cross domains adversarial learning (CDAL) for Chinese named entity recognition is proposed to further improve the performance, which not only uses the pre-trained basic framework unit, but also uses the adversarial multi-task learning on both electronic medical record texts and online medical consultation texts. Experimental results validate the effectiveness of CDAL.

Label-indicator morpheme growth on LSTM for Chinese healthcare question department classification.

BACKGROUND: Current Chinese medicine has an urgent demand for convenient medical services. When facing a large number of patients, understanding patients' questions automatically and precisely is useful. Different from the high professional medical text, patients' questions contain only a small amount of descriptions regarding the symptoms, and the questions are slightly professional and colloquial. OBJECT: The aim of this paper is to implement a department classification system for patient questions. Patients' questions will be classified into 11 departments, such as surgery and others. METHODS: This paper presents a morpheme growth model that enhances the memories of key elements in questions, and later extracts the "label-indicators" and germinates the expansion vectors around them. Finally, the model inputs the expansion vectors into a neural network to assign department labels for patients' questions. RESULTS: All compared methods are validated by experiments on three datasets that are composed of real patient questions. The proposed method has some ability to improve the performance of the classification. CONCLUSIONS: The proposed method is effective for the departments classification of patients questions and serves as a useful system for the automatic understanding of patient questions.

Intelligent personalized diagnosis modeling in advanced medical system for Parkinson's disease using voice signals.

Currently, machine learning methods have been utilized to realize the early detection of Parkinson's disease (PD) by using voice signals. Because the vocal system of each person is unique, and the same person's pronunciation can be different at different times, the training samples used in machine learning become very different from the speech signal of the patient to be diagnosed, frequently resulting in poor diagnostic performance. On this account, this paper presents a new intelligent personalized diagnosis method (PDM) for Parkinson's disease. The method was designed to begin with constructing new training data by assigning the best classifier to each training sample composed of features from the speech signals of patients. Subsequently, a meta-classifier was trained on the new training data. Finally, for the signal of each test patient, the method used the meta-classifier to select the most appropriate classifier, followed by adopting the selected classifier to classify the signal so that the more accurate diagnosis result of the test patient can be obtained. The novelty of the proposed method is that the proposed method uses different classifiers to perform the diagnosis of PD for diversified patients, whereas the current method uses the same classifier to diagnose all patients to be tested. Results of a large number of experiments show that PDM not only improves the performance but also exceeds the existing methods in speed.

MLP-Like Model With Convolution Complex Transformation for Auxiliary Diagnosis Through Medical Images.

Medical images such as facial and tongue images have been widely used for intelligence-assisted diagnosis, which can be regarded as the multi-label classification task for disease location (DL) and disease nature (DN) of biomedical images. Compared with complicated convolutional neural networks and Transformers for this task, recent MLP-like architectures are not only simple and less computationally expensive, but also have stronger generalization capabilities. However, MLP-like models require better input features from the image. Thus, this study proposes a novel convolution complex transformation MLP-like (CCT-MLP) model for the multi-label DL and DN recognition task for facial and tongue images. Notably, the convolutional Tokenizer and multiple convolutional layers are first used to extract the better shallow features from input biomedical images to make up for the loss of spatial information obtained by the simple MLP structure. Subsequently, the Channel-MLP architecture with complex transformations is used to extract deep-level contextual features. In this way, multi-channel features are extracted and mixed to perform the multi-label classification of the input biomedical images. Experimental results on our constructed multi-label facial and tongue image datasets demonstrate that our method outperforms existing methods in terms of both accuracy (Acc) and mean average precision (mAP).

Personalized Intelligent Syndrome Differentiation Guided By TCM Consultation Philosophy.

Traditional Chinese Medicine (TCM) is one of the oldest medical systems in the world, and inquiry is an essential part of TCM diagnosis. The development of artificial intelligence has led to the proposal of several computational TCM diagnostic methods. However, there are few research studies among them, and they have the following flaws: (1) insufficient engagement with the patient, (2) barren TCM consultation philosophy, and (3) inadequate validation of the method. As TCM inquiry knowledge is abstract and there are few relevant datasets, we devise a novel knowledge representation technique. The mapping of symptoms and syndromes is constructed based on the diagnostics of traditional Chinese medicine. As a guide, the inquiry knowledge base is constructed utilizing the "Ten Brief Inquiries," TCM's domain knowledge. Subsequently, a corresponding assessment approach is proposed for an intelligent consultation model for syndrome differentiation. We establish three criteria: the quality of the generated question-answer pairs, the accuracy of model identification, and the average number of questions. Three TCM specialists are asked to undertake a manual evaluation of the model separately. The results reveal that our approach is capable of pretty accurate syndrome differentiation. Furthermore, the model's question and answer pairs for simulated consultations are relevant, accurate, and efficient.

Task-Coupling Elastic Learning for Physical Sign-Based Medical Image Classification.

Physical signs of patients indicate crucial evidence for diagnosing both location and nature of the disease, where there is a sequential relationship between the two tasks. Thus their joint learning can utilize intrinsic association by transferring related knowledge across relevant tasks. Choosing the right time to transfer is a critical problem for joint learning. However, how to dynamically adjust when tasks interact to capture the right time for transferring related knowledge is still an open issue. To this end, we propose a Task-Coupling Elastic Learning (TCEL) framework to model the task relatedness for classifying disease-location and disease-nature based on physical sign images. The main idea is to dynamically transfer relevant knowledge by progressively shifting task-coupling from loose to tight during the multi-stage training. In the early stage of training, we relax the constraints of modeling relations to focus more in learning the generic task-common features. In the later stage, the semantic guidance will be strengthened to learn the task-specific features. Specifically, a dynamic sequential module (DSM) is proposed to explicitly model the sequential relationship and enable multi-stage training. Moreover, to address the side effect of DSM, a new loss regularization is proposed. The extensive experiments on these two clinical datasets show the superiority of the proposed method over the baselines, and demonstrate the effectiveness of the proposed task-coupling elastic mechanism.

Inner-Imaging Networks: Put Lenses Into Convolutional Structure.

Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previous works address that by enhancing the diversities of filters, they have not considered the complementarity and the completeness of the internal convolutional structure. To respond to this problem, we propose a novel inner-imaging (InI) architecture, which allows relationships between channels to meet the above requirement. Specifically, we organize the channel signal points in groups using convolutional kernels to model both the intragroup and intergroup relationships simultaneously. A convolutional filter is a powerful tool for modeling spatial relations and organizing grouped signals, so the proposed methods map the channel signals onto a pseudoimage, like putting a lens into the internal convolution structure. Consequently, not only is the diversity of channels increased but also the complementarity and completeness can be explicitly enhanced. The proposed architecture is lightweight and easy to be implement. It provides an efficient self-organization strategy for convolutional networks to improve their performance. Extensive experiments are conducted on multiple benchmark datasets, including CIFAR, SVHN, and ImageNet. Experimental results verify the effectiveness of the InI mechanism with the most popular convolutional networks as the backbones.

Acupuncture combined with cognitive-behavioural therapy for insomnia (CBT-I) in patients with insomnia: study protocol for a randomised controlled trial.

INTRODUCTION: Insomnia affects physical and mental health due to the lack of continuous and complete sleep architecture. Polysomnograms (PSGs) are used to record electrical information to perform sleep architecture using deep learning. Although acupuncture combined with cognitive-behavioural therapy for insomnia (CBT-I) could not only improve sleep quality, solve anxiety, depression but also ameliorate poor sleep habits and detrimental cognition. Therefore, this study will focus on the effects of electroacupuncture combined with CBT-I on sleep architecture with deep learning. METHODS AND ANALYSIS: This randomised controlled trial will evaluate the efficacy and effectiveness of electroacupuncture combined with CBT-I in patients with insomnia. Participants will be randomised to receive either electroacupuncture combined with CBT-I or sham acupuncture combined with CBT-I and followed up for 4 weeks. The primary outcome is sleep quality, which is evaluated by the Pittsburgh Sleep Quality Index. The secondary outcome measures include a measurement of depression severity, anxiety, maladaptive cognitions associated with sleep and adverse events. Sleep architecture will be assessed using deep learning on PSGs. ETHICS AND DISSEMINATION: This trial has been approved by the institutional review boards and ethics committees of the First Affiliated Hospital of Sun Yat-sun University (2021763). The results will be disseminated through peer-reviewed journals. The results of this trial will be disseminated through peer-reviewed publications and conference abstracts or posters. TRIAL REGISTRATION NUMBER: CTR2100052502.

[A classification method of gene expression profile based on a locally linear embedding algorism with improved distance].

With its high dimensionalities, small samples and great noise, feature reduction of gene expression profile becomes quite necessary. The most common form of gene expression profile is nonlinear, and traditional dimensionality reduction methods can not project high dimensional data, whose initial dimensionalities are low, into low dimensional space. In this work, an improved distance locally linear embedding (LLE ) algorism was proposed to reduce the dimensionalities. LLE method is very sensitive to the closely-neighboring parameters. In order to enhance the robustness to the number of neighbors, in the paper we presented a novel distance to measure the distance between the samples for the purpose of reducing-the influence of distribution of samples. Experimental results demonstrated that the improved distance LLE can effectively extract information of classification features and greatly reduce the dimensionalities of data while maintaining a higher classification accuracy.

The complete chloroplast genome of Arcangelisia gusanlung H.S.Lo (Menispermaceae), an important traditional medicine from South China.

Arcangelisia gusanlung H.S.Lo is widely used as a folk medicine by the Dai and Li peoples. Here, we report the first complete chloroplast (cp) genome sequence for this species based on Illumina paired-end sequencing data. The cp genome was 162,509 bp in length with a small single-copy (SSC) region of 20,852 bp, a large single-copy (LSC) region of 91,449 bp, and two separated inverted region of 25,104 bp. In total, 129 unique genes were identified of this genome, including 84 protein-coding genes, 37 tRNA genes, and eight rRNA genes. The GC contents of this genome is 37.8%. Phylogenetic analysis based on 13 complete cp genomes showed a strong sister relationship with Tinospora cordifolia (Willd.) Miers and Tinospora sinensis (Lour.) Merr. This complete genome of A. gusanlung will provide valuable information to elucidate the mechanism of speciation of Arcangelisia Becc.

Automatic Construction of Chinese Herbal Prescriptions From Tongue Images Using CNNs and Auxiliary Latent Therapy Topics.

The tongue image provides important physical information of humans. It is of great importance for diagnoses and treatments in clinical medicine. Herbal prescriptions are simple, noninvasive, and have low side effects. Thus, they are widely applied in China. Studies on the automatic construction technology of herbal prescriptions based on tongue images have great significance for deep learning to explore the relevance of tongue images for herbal prescriptions, it can be applied to healthcare services in mobile medical systems. In order to adapt to the tongue image in a variety of photographic environments and construct herbal prescriptions, a neural network framework for prescription construction is designed. It includes single/double convolution channels and fully connected layers. Furthermore, it proposes the auxiliary therapy topic loss mechanism to model the therapy of Chinese doctors and alleviate the interference of sparse output labels on the diversity of results. The experiment use the real-world tongue images and the corresponding prescriptions and the results can generate prescriptions that are close to the real samples, which verifies the feasibility of the proposed method for the automatic construction of herbal prescriptions from tongue images. Also, it provides a reference for automatic herbal prescription construction from more physical information.

Fully-channel regional attention network for disease-location recognition with tongue images.

OBJECTIVE: Using the deep learning model to realize tongue image-based disease location recognition and focus on solving two problems: 1. The ability of the general convolution network to model detailed regional tongue features is weak; 2. Ignoring the group relationship between convolution channels, which caused the high redundancy of the model. METHODS: To enhance the convolutional neural networks. In this paper, a stochastic region pooling method is proposed to gain detailed regional features. Also, an inner-imaging channel relationship modeling method is proposed to model multi-region relations on all channels. Moreover, we combine it with the spatial attention mechanism. RESULTS: The tongue image dataset with the clinical disease-location label is established. Abundant experiments are carried out on it. The experimental results show that the proposed method can effectively model the regional details of tongue image and improve the performance of disease location recognition. CONCLUSION: In this paper, we construct the tongue image dataset with disease-location labels to mine the relationship between tongue images and disease locations. A novel fully-channel regional attention network is proposed to model the local detail tongue features and improve the modeling efficiency. SIGNIFICANCE: The applications of deep learning in tongue image disease-location recognition and the proposed innovative models have guiding significance for other assistant diagnostic tasks. The proposed model provides an example of efficient modeling of detailed tongue features, which is of great guiding significance for other auxiliary diagnosis applications.

Grouping attributes zero-shot learning for tongue constitution recognition.

Traditional Chinese Medicine (TCM) considers that the personal constitution determines the occurrence trend and therapeutic effects of certain diseases, which can be recognized by machine learning through tongue images. However, current machine learning methods are confronted with two challenges. First, there are not some larger tongue image databases available. Second, they do not use the domain knowledge of TCM, so that the imbalance of constitution categories cannot be solved. Therefore, this paper proposes a new constitution recognition method based on the zero-shot learning with the knowledge of TCM. To further improve the performance, a new zero-shot learning method is proposed by grouping attributes and learning discriminant latent features, which can better solve the imbalance problem of constitution categories. Experimental results on our constructed databases validate the proposed methods.

Personalized Body Constitution Inquiry Based on Machine Learning.

Background: Body constitution (BC) is the abstract concept indicating the state of a person's health in Traditional Chinese Medicine (TCM). The doctor identifies the body constitution of the patient through inspection and inquiry. Previous research simulates doctors to identify BC types according to a patient's objective physical indicators. However, the lack of subjective feeling information can reduce the accuracy of the machine to imitate the doctor's diagnosis. The Constitution in Chinese Medicine Questionnaire (CCMQ) is used to collect subjective information but suffers from low acquisition efficiency. Methods: This paper presents a personalized body constitution inquiry method based on a machine learning technique. It employs a random generator, a feature extractor, and a classifier to simulate the doctor inquiry and generate a personalized questionnaire. Specifically, the feature extractor evaluates and sorts the question of the constitution in the CCMQ based on the recognition results of the tongue coating image of patients. The sorted questions and relevant BC label are inputted into the classifier; the best questions are screened out for patients. Results: The experimental results show that our method can select personalized questions from the CCMQ for the patients, significantly reducing the time and the number of questions to answer. It also improves the accuracy of recognizing BC. Compared with the CCMQ, patients had 68.3% fewer questions to answer and the time occupied by answering is reduced by 80.3%. Conclusions: The proposed method can simulate the doctor's inquiry and pick out personalized questions for patients. It can act as auxiliary diagnosis tools to collect subjective patient feelings and help make further judgments on the patient's BC types.

Multilevel and Multiscale Feature Aggregation in Deep Networks for Facial Constitution Classification.

Constitution classification is the basis and core content of TCM constitution research. In order to improve the accuracy of constitution classification, this paper proposes a multilevel and multiscale features aggregation method within the convolutional neural network, which consists of four steps. First, it uses the pretrained VGG16 as the basic network and then refines the network structure through supervised feature learning so as to capture local image features. Second, it extracts the image features of different layers from the fine-tuned VGG16 model, which are then dimensionally reduced by principal component analysis (PCA). Third, it uses another pretrained NASNetMobile network for supervised feature learning, where the previous layer features of the global average pooling layer are outputted. Similarly, these features are dimensionally reduced by PCA and then are fused with the features of different layers in VGG16 after the PCA. Finally, all features are aggregated with the fully connected layers of the fine-tuned VGG16, and then the constitution classification is performed. The conducted experiments show that using the multilevel and multiscale feature aggregation is very effective in the constitution classification, and the accuracy on the test dataset reaches 69.61%.

Complexity perception classification method for tongue constitution recognition.

The body constitution is much related to the diseases and the corresponding treatment programs in Traditional Chinese Medicine. It can be recognized by the tongue image diagnosis, so that it is essentially regarded as a problem of tongue image classification, where each tongue image is classified into one of nine constitution types. This paper first presents a system framework to automatically identify the constitution through natural tongue images, where deep convolutional neural networks are carefully designed for tongue coating detection, tongue coating calibration, and constitution recognition. Under the system framework, a novel complexity perception (CP) classification method is proposed to nicely perform the constitution recognition, which can better deal with the bad influence of the variation of environmental condition and the uneven distribution of the tongue images on constitution recognition performance. CP performs the constitution recognition based on the complexity of individual tongue images by selecting the classifier with the corresponding complexity. To evaluate the performance of the proposed method, experiments are conducted on three sizes of clinic tongue images from hospitals. The experimental results illustrate that CP is effective to improve the accuracy of body constitution recognition.

Prussian blue analogue-derived Ni and Co bimetallic oxide nanoplate arrays block-built from porous and hollow nanocubes for the efficient oxygen evolution reaction.

Effective oxygen evolution reaction (OER) catalysts composed of Earth-abundant transition metals are crucial for sustainable energy conversion and storage. Metal-organic frameworks (MOFs) with tunable compositions are promising precursors for the fabrication of hollow and porous electrocatalysts. However, pulverous MOFs usually suffer from agglomeration during pyrolysis, greatly reducing the activity of their derived catalysts. In this work, Prussian blue analogue (PBA) arrays with hierarchical multidimensional architecture were directly grown on nickel foam (NF) using a template-oriented method. The subsequent calcination in air allowed for obtaining NixCo3-xO4 nanoplate arrays consisting of porous and hollow nanocubes. The derived bimetallic NixCo3-xO4/NF required only an overpotential of 287 mV to achieve a current density of 10 mA cm-2 in 1.0 M KOH solution, which is much lower than that of the monometallic NiO and the RuO2 benchmark. The 3D intersectional architecture of the NixCo3-xO4 nanoplates and the porous and hollow nanocube subunits contributed to the large specific surface area and reduced charge-transfer resistance of the NixCo3-xO4/NF electrode. Density functional theory (DFT) calculations and post-OER characterization revealed that the incorporated Co was the active sites and electrochemical active CoOOH intermediates were in situ formed during the OER. Our study provides a facile and efficient strategy for the rational design of MOF-derived materials towards effective and low-cost electrocatalysis.

Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image.

Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.